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SQP and ESRA grant 4 scholarships for the course

Short description of the course:
This course discusses the problem of measurement errors in survey research. It will be shown what the consequences of the errors are in the results of the research. It will also be shown how large the differences are in results assuming perfect measurement and taking into account the fact that there are measurement errors.

This requires that we are able to estimate or predict the quality of survey questions. In this course two approaches will be discussed. The first is the estimation of the reliability and validity of questions using multitrait-multimethod (MTMM) experiments. The second is the prediction of the reliability and validity of questions using the software Survey Quality Predictor (SQP 2.1) which has been developed on the basis of the evaluation of more than 3,700 questions using MTMM experiments.

It will be shown how to use the software to predict a quality estimate, and how to use these quality estimates to correct for measurement errors in the analysis of the relationships between variables.
Scholarship objective: To promote the good practice of correcting for measurement error.

Scholarship value/inclusions:
• €250 for registration and/or travel costs.
• A tutor from the SQP team will be assigned to assist the candidate’s development of the final study.
• Publication of final study (first) as RECSM working paper.

Target candidates: Students or scholars who have already developed an empirical study and would like to correct for measurement error. Candidates must hold a master degree (minimum).

Requirements:
• Submission of a report on the state of the study in September 2016.
• Submission of the final study as RECSM working paper and/or for a peer-reviewed journal not later than in December 2016.

Criteria for the attribution of the grant:
• Relevance of the research question of the candidate.
• Concrete plan of applying the course knowledge for a peer-reviewed publication.

Application instructions:
Applicants should submit their CV as well as an abstract of max. 500 words, specifying their research question, summarizing its relevance, describing the data and variables, and highlighting how they plan to apply the course knowledge.